Robotic Motion Intelligence Using Vector Symbolic Architectures and Blockchain-Based Smart Contracts

• Published in: Robotics (Basel) Vol. 14; no. 4; p. 38
• Main Authors: De Silva, Daswin, Withanage, Sudheera, Sumanasena, Vidura, Gunasekara, Lakshitha, Moraliyage, Harsha, Mills, Nishan, Manic, Milos
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2025
• Subjects: Algorithms; Artificial intelligence; Behavior; Blockchain; Contracts; Data mining; Datasets; Deep learning; Distributed ledger; Energy consumption; Explainable artificial intelligence; Generative artificial intelligence; Machine learning; Motion; Movement; Neural networks; Noise reduction; Power management; robotic learning; robotic motion intelligence; Robotic surgery; Robots; Safety management; Semantics; vector symbolic architecture; Australia
• ISSN: 2218-6581; 2218-6581
• DOI: 10.3390/robotics14040038

The rapid adoption of artificial intelligence (AI) systems, such as predictive AI, generative AI, and explainable AI, is in contrast to the slower development and uptake of robotic AI systems. Dynamic environments, sensory processing, mechanical movements, power management, and safety are inherent complexities of robotic intelligence capabilities that can be addressed using novel AI approaches. The current AI landscape is dominated by machine learning techniques, specifically deep learning algorithms, that have been effective in addressing some of these challenges. However, these algorithms are subject to computationally complex processing and operational needs such as high data dependency. In this paper, we propose a computation-efficient and data-efficient framework for robotic motion intelligence (RMI) based on vector symbolic architectures (VSAs) and blockchain-based smart contracts. The capabilities of VSAs are leveraged for computationally efficient learning and noise suppression during perception, motion, movement, and decision-making tasks. As a distributed ledger technology, smart contracts address data dependency through a decentralized, distributed, and secure transactions ledger that satisfies contractual conditions. An empirical evaluation of the framework confirms its value and contribution towards addressing the practical challenges of robotic motion intelligence by significantly reducing the learnable parameters by 10 times while preserving sufficient accuracy compared to existing deep learning solutions.